Overview

Dataset statistics

Number of variables16
Number of observations412722
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.4 MiB
Average record size in memory128.0 B

Variable types

Numeric13
DateTime1
Categorical2

Alerts

markdown1 is highly overall correlated with markdown2 and 3 other fieldsHigh correlation
markdown2 is highly overall correlated with markdown1 and 3 other fieldsHigh correlation
markdown3 is highly overall correlated with markdown1 and 3 other fieldsHigh correlation
markdown4 is highly overall correlated with markdown1 and 3 other fieldsHigh correlation
markdown5 is highly overall correlated with markdown1 and 3 other fieldsHigh correlation
size is highly overall correlated with typeHigh correlation
store is highly overall correlated with typeHigh correlation
type is highly overall correlated with size and 1 other fieldsHigh correlation
holiday is highly imbalanced (63.4%)Imbalance
markdown1 has 265389 (64.3%) zerosZeros
markdown2 has 305692 (74.1%) zerosZeros
markdown3 has 279200 (67.6%) zerosZeros
markdown4 has 281031 (68.1%) zerosZeros
markdown5 has 264638 (64.1%) zerosZeros

Reproduction

Analysis started2025-03-10 16:24:33.679614
Analysis finished2025-03-10 16:25:04.760516
Duration31.08 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

store
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.295819
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:04.837613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile43
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.785237
Coefficient of variation (CV)0.57343652
Kurtosis-1.1490385
Mean22.295819
Median Absolute Deviation (MAD)11
Skewness0.070698815
Sum9201975
Variance163.46228
MonotonicityNot monotonic
2025-03-10T22:10:04.932952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
34 10213
 
2.5%
32 9973
 
2.4%
11 9967
 
2.4%
23 9959
 
2.4%
24 9949
 
2.4%
6 9902
 
2.4%
15 9895
 
2.4%
8 9886
 
2.4%
40 9878
 
2.4%
28 9873
 
2.4%
Other values (35) 313227
75.9%
ValueCountFrequency (%)
1 9830
2.4%
2 9622
2.3%
3 8904
2.2%
4 9569
2.3%
5 8997
2.2%
6 9902
2.4%
7 9758
2.4%
8 9886
2.4%
9 8834
2.1%
10 9606
2.3%
ValueCountFrequency (%)
45 9629
2.3%
44 7169
1.7%
43 6671
1.6%
42 6885
1.7%
41 9832
2.4%
40 9878
2.4%
39 9532
2.3%
38 7356
1.8%
37 7192
1.7%
36 6222
1.5%

date
Date

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum2010-02-05 00:00:00
Maximum2012-10-26 00:00:00
2025-03-10T22:10:05.055329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:05.155089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temperature
Real number (ℝ)

Distinct3528
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.110237
Minimum-2.06
Maximum100.14
Zeros0
Zeros (%)0.0%
Negative69
Negative (%)< 0.1%
Memory size3.1 MiB
2025-03-10T22:10:05.266077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.06
5-th percentile27.28
Q146.73
median62.11
Q374.29
95-th percentile87.27
Maximum100.14
Range102.2
Interquartile range (IQR)27.56

Descriptive statistics

Standard deviation18.454227
Coefficient of variation (CV)0.30700639
Kurtosis-0.63099911
Mean60.110237
Median Absolute Deviation (MAD)13.61
Skewness-0.3238073
Sum24808817
Variance340.5585
MonotonicityNot monotonic
2025-03-10T22:10:05.376500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.43 697
 
0.2%
67.87 636
 
0.2%
72.62 574
 
0.1%
76.67 568
 
0.1%
70.28 551
 
0.1%
76.03 535
 
0.1%
50.56 532
 
0.1%
64.05 530
 
0.1%
64.21 514
 
0.1%
50.81 474
 
0.1%
Other values (3518) 407111
98.6%
ValueCountFrequency (%)
-2.06 69
< 0.1%
5.54 67
< 0.1%
6.23 67
< 0.1%
7.46 69
< 0.1%
9.51 69
< 0.1%
9.55 67
< 0.1%
10.09 66
< 0.1%
10.11 68
< 0.1%
10.24 69
< 0.1%
10.53 72
< 0.1%
ValueCountFrequency (%)
100.14 44
 
< 0.1%
100.07 46
 
< 0.1%
99.66 48
 
< 0.1%
99.22 184
< 0.1%
99.2 46
 
< 0.1%
98.43 43
 
< 0.1%
98.15 47
 
< 0.1%
97.66 42
 
< 0.1%
97.6 48
 
< 0.1%
97.18 186
< 0.1%

fuel_price
Real number (ℝ)

Distinct892
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3611193
Minimum2.472
Maximum4.468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:05.487240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.472
5-th percentile2.653
Q12.932
median3.452
Q33.738
95-th percentile4.029
Maximum4.468
Range1.996
Interquartile range (IQR)0.806

Descriptive statistics

Standard deviation0.45881301
Coefficient of variation (CV)0.13650602
Kurtosis-1.1869028
Mean3.3611193
Median Absolute Deviation (MAD)0.375
Skewness-0.10584778
Sum1387207.9
Variance0.21050938
MonotonicityNot monotonic
2025-03-10T22:10:05.614268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.638 2501
 
0.6%
3.63 2112
 
0.5%
2.771 1884
 
0.5%
3.891 1812
 
0.4%
3.594 1768
 
0.4%
3.524 1764
 
0.4%
3.523 1761
 
0.4%
2.72 1761
 
0.4%
3.666 1745
 
0.4%
2.78 1621
 
0.4%
Other values (882) 393993
95.5%
ValueCountFrequency (%)
2.472 38
 
< 0.1%
2.513 45
 
< 0.1%
2.514 886
0.2%
2.52 39
 
< 0.1%
2.533 42
 
< 0.1%
2.539 37
 
< 0.1%
2.54 142
 
< 0.1%
2.542 45
 
< 0.1%
2.545 38
 
< 0.1%
2.548 879
0.2%
ValueCountFrequency (%)
4.468 360
0.1%
4.449 352
0.1%
4.308 163
< 0.1%
4.301 355
0.1%
4.294 358
0.1%
4.293 191
< 0.1%
4.288 167
< 0.1%
4.282 165
< 0.1%
4.277 350
0.1%
4.273 358
0.1%

markdown1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2278
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2568.5946
Minimum0
Maximum88646.76
Zeros265389
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:05.741110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32779.97
95-th percentile12297.04
Maximum88646.76
Range88646.76
Interquartile range (IQR)2779.97

Descriptive statistics

Standard deviation6013.5604
Coefficient of variation (CV)2.3411871
Kurtosis35.171293
Mean2568.5946
Median Absolute Deviation (MAD)0
Skewness4.743194
Sum1.0601155 × 109
Variance36162909
MonotonicityNot monotonic
2025-03-10T22:10:05.854881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265389
64.3%
460.73 102
 
< 0.1%
1.5 102
 
< 0.1%
175.64 93
 
< 0.1%
6438.2 74
 
< 0.1%
2732.08 73
 
< 0.1%
708.33 73
 
< 0.1%
4611.57 73
 
< 0.1%
16404.25 72
 
< 0.1%
11028.34 72
 
< 0.1%
Other values (2268) 146599
35.5%
ValueCountFrequency (%)
0 265389
64.3%
0.27 51
 
< 0.1%
0.5 49
 
< 0.1%
1.5 102
 
< 0.1%
1.94 50
 
< 0.1%
2.12 51
 
< 0.1%
2.4 49
 
< 0.1%
2.42 49
 
< 0.1%
2.43 51
 
< 0.1%
2.8 50
 
< 0.1%
ValueCountFrequency (%)
88646.76 68
< 0.1%
78124.5 64
< 0.1%
75149.79 66
< 0.1%
65021.23 71
< 0.1%
62567.6 66
< 0.1%
62172.73 69
< 0.1%
60740.64 70
< 0.1%
60394.73 68
< 0.1%
58928.52 63
< 0.1%
56917.7 71
< 0.1%

markdown2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1481
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean871.95063
Minimum0
Maximum104519.54
Zeros305692
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:05.955127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.91
95-th percentile3744.31
Maximum104519.54
Range104519.54
Interquartile range (IQR)1.91

Descriptive statistics

Standard deviation5045.0042
Coefficient of variation (CV)5.7858828
Kurtosis145.62963
Mean871.95063
Median Absolute Deviation (MAD)0
Skewness10.650619
Sum3.5987321 × 108
Variance25452067
MonotonicityNot monotonic
2025-03-10T22:10:06.077675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 305692
74.1%
1.91 533
 
0.1%
3 492
 
0.1%
0.5 482
 
0.1%
1.5 466
 
0.1%
4 365
 
0.1%
6 361
 
0.1%
3.82 346
 
0.1%
7.64 345
 
0.1%
19 339
 
0.1%
Other values (1471) 103301
 
25.0%
ValueCountFrequency (%)
0 305692
74.1%
0.02 96
 
< 0.1%
0.03 206
 
< 0.1%
0.09 137
 
< 0.1%
0.11 68
 
< 0.1%
0.15 138
 
< 0.1%
0.18 205
 
< 0.1%
0.24 131
 
< 0.1%
0.27 68
 
< 0.1%
0.3 135
 
< 0.1%
ValueCountFrequency (%)
104519.54 67
< 0.1%
97740.99 68
< 0.1%
92523.94 68
< 0.1%
89121.94 70
< 0.1%
82881.16 71
< 0.1%
72413.71 69
< 0.1%
70574.85 66
< 0.1%
58804.91 69
< 0.1%
58046.41 71
< 0.1%
56106.2 70
< 0.1%

markdown3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1658
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.59872
Minimum0
Maximum141630.61
Zeros279200
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:06.188346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.33
95-th percentile210.34
Maximum141630.61
Range141630.61
Interquartile range (IQR)4.33

Descriptive statistics

Standard deviation5407.7388
Coefficient of variation (CV)11.974655
Kurtosis256.34454
Mean451.59872
Median Absolute Deviation (MAD)0
Skewness15.159304
Sum1.8638473 × 108
Variance29243639
MonotonicityNot monotonic
2025-03-10T22:10:06.299242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 279200
67.6%
3 740
 
0.2%
6 691
 
0.2%
2 656
 
0.2%
1 607
 
0.1%
0.22 473
 
0.1%
0.5 463
 
0.1%
4 437
 
0.1%
0.01 433
 
0.1%
3.2 373
 
0.1%
Other values (1648) 128649
31.2%
ValueCountFrequency (%)
0 279200
67.6%
0.01 433
 
0.1%
0.02 120
 
< 0.1%
0.04 238
 
0.1%
0.05 69
 
< 0.1%
0.06 203
 
< 0.1%
0.09 69
 
< 0.1%
0.12 68
 
< 0.1%
0.13 53
 
< 0.1%
0.15 247
 
0.1%
ValueCountFrequency (%)
141630.61 67
< 0.1%
109030.75 69
< 0.1%
103991.94 69
< 0.1%
101378.79 66
< 0.1%
89402.64 65
< 0.1%
88805.58 68
< 0.1%
83340.33 66
< 0.1%
83192.81 69
< 0.1%
79621.2 66
< 0.1%
77451.26 66
< 0.1%

markdown4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1945
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1070.9479
Minimum0
Maximum67474.85
Zeros281031
Zeros (%)68.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:06.410222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3421.71
95-th percentile5124.86
Maximum67474.85
Range67474.85
Interquartile range (IQR)421.71

Descriptive statistics

Standard deviation3861.6797
Coefficient of variation (CV)3.6058521
Kurtosis87.202343
Mean1070.9479
Median Absolute Deviation (MAD)0
Skewness8.1165747
Sum4.4200376 × 108
Variance14912570
MonotonicityNot monotonic
2025-03-10T22:10:06.521047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 281031
68.1%
9 274
 
0.1%
4 196
 
< 0.1%
2 188
 
< 0.1%
3 142
 
< 0.1%
47 141
 
< 0.1%
657.56 139
 
< 0.1%
1330.36 138
 
< 0.1%
67.72 137
 
< 0.1%
17 137
 
< 0.1%
Other values (1935) 130199
31.5%
ValueCountFrequency (%)
0 281031
68.1%
0.22 57
 
< 0.1%
0.41 52
 
< 0.1%
0.46 48
 
< 0.1%
0.78 52
 
< 0.1%
0.87 49
 
< 0.1%
0.92 45
 
< 0.1%
1.5 55
 
< 0.1%
1.88 48
 
< 0.1%
1.98 44
 
< 0.1%
ValueCountFrequency (%)
67474.85 69
< 0.1%
57817.56 68
< 0.1%
57815.43 68
< 0.1%
53603.99 63
< 0.1%
52739.02 69
< 0.1%
48403.53 70
< 0.1%
48159.86 66
< 0.1%
48086.64 65
< 0.1%
47452.43 71
< 0.1%
46238.28 69
< 0.1%

markdown5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2294
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1647.0238
Minimum0
Maximum108519.28
Zeros264638
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:06.647967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32143.91
95-th percentile7373.13
Maximum108519.28
Range108519.28
Interquartile range (IQR)2143.91

Descriptive statistics

Standard deviation4180.2215
Coefficient of variation (CV)2.5380456
Kurtosis185.87731
Mean1647.0238
Median Absolute Deviation (MAD)0
Skewness10.034612
Sum6.7976296 × 108
Variance17474252
MonotonicityNot monotonic
2025-03-10T22:10:06.759071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 264638
64.1%
2743.18 136
 
< 0.1%
1064.56 120
 
< 0.1%
3839.19 74
 
< 0.1%
2116.1 73
 
< 0.1%
17316.01 73
 
< 0.1%
2456.79 73
 
< 0.1%
28803.28 72
 
< 0.1%
4169.76 72
 
< 0.1%
653.02 72
 
< 0.1%
Other values (2284) 147319
35.7%
ValueCountFrequency (%)
0 264638
64.1%
135.16 64
 
< 0.1%
153.04 46
 
< 0.1%
153.9 49
 
< 0.1%
164.08 52
 
< 0.1%
170.64 68
 
< 0.1%
171.76 70
 
< 0.1%
180.07 64
 
< 0.1%
212.75 49
 
< 0.1%
224.86 50
 
< 0.1%
ValueCountFrequency (%)
108519.28 65
< 0.1%
105223.11 68
< 0.1%
85851.87 66
< 0.1%
63005.58 66
< 0.1%
58068.14 69
< 0.1%
57029.78 68
< 0.1%
53212.72 68
< 0.1%
37581.27 70
< 0.1%
36430.33 68
< 0.1%
36360.42 65
< 0.1%

cpi
Real number (ℝ)

Distinct2145
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.20171
Minimum126.064
Maximum227.23281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:07.012207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum126.064
5-th percentile126.49626
Q1132.06443
median182.31878
Q3212.51859
95-th percentile221.94916
Maximum227.23281
Range101.16881
Interquartile range (IQR)80.45416

Descriptive statistics

Standard deviation39.167528
Coefficient of variation (CV)0.22878001
Kurtosis-1.8298277
Mean171.20171
Median Absolute Deviation (MAD)41.483671
Skewness0.086115801
Sum70658711
Variance1534.0952
MonotonicityNot monotonic
2025-03-10T22:10:07.122616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.0710323 691
 
0.2%
131.1083333 690
 
0.2%
131.043 688
 
0.2%
128.9998667 688
 
0.2%
131.0756667 687
 
0.2%
130.683 687
 
0.2%
130.737871 686
 
0.2%
131.1453333 686
 
0.2%
130.7929 686
 
0.2%
129.8459667 686
 
0.2%
Other values (2135) 405847
98.3%
ValueCountFrequency (%)
126.064 665
0.2%
126.0766452 665
0.2%
126.0854516 660
0.2%
126.0892903 670
0.2%
126.1019355 672
0.2%
126.1069032 669
0.2%
126.1119032 668
0.2%
126.114 674
0.2%
126.1145806 673
0.2%
126.1266 673
0.2%
ValueCountFrequency (%)
227.2328068 63
< 0.1%
227.214288 62
< 0.1%
227.1693919 63
< 0.1%
227.0369359 70
< 0.1%
227.0184166 69
< 0.1%
226.9873637 134
< 0.1%
226.9735448 69
< 0.1%
226.9688442 133
< 0.1%
226.9662325 63
< 0.1%
226.9239785 133
< 0.1%

unemployment
Real number (ℝ)

Distinct349
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9655219
Minimum3.879
Maximum14.313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:07.233132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.879
5-th percentile5.326
Q16.891
median7.866
Q38.572
95-th percentile12.187
Maximum14.313
Range10.434
Interquartile range (IQR)1.681

Descriptive statistics

Standard deviation1.8700957
Coefficient of variation (CV)0.23477378
Kurtosis2.690108
Mean7.9655219
Median Absolute Deviation (MAD)0.859
Skewness1.1817528
Sum3287546.1
Variance3.4972578
MonotonicityNot monotonic
2025-03-10T22:10:07.359830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.099 5044
 
1.2%
7.852 3522
 
0.9%
8.163 3520
 
0.9%
7.343 3322
 
0.8%
7.931 3310
 
0.8%
7.057 3304
 
0.8%
6.565 3285
 
0.8%
7.441 3279
 
0.8%
8.2 3273
 
0.8%
6.891 3267
 
0.8%
Other values (339) 377596
91.5%
ValueCountFrequency (%)
3.879 265
 
0.1%
4.077 873
0.2%
4.125 1816
0.4%
4.145 557
 
0.1%
4.156 1795
0.4%
4.261 1813
0.4%
4.308 868
0.2%
4.42 1814
0.4%
4.584 1969
0.5%
4.607 854
0.2%
ValueCountFrequency (%)
14.313 2605
0.6%
14.18 2404
0.6%
14.099 2422
0.6%
14.021 2242
0.5%
13.975 1509
0.4%
13.736 2445
0.6%
13.503 2637
0.6%
12.89 2456
0.6%
12.187 2474
0.6%
11.627 2478
0.6%

dept
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.648211
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:07.457591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median36
Q372
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)54

Descriptive statistics

Standard deviation30.19218
Coefficient of variation (CV)0.69171633
Kurtosis-1.176012
Mean43.648211
Median Absolute Deviation (MAD)23
Skewness0.38401241
Sum18014577
Variance911.56775
MonotonicityNot monotonic
2025-03-10T22:10:07.581634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6435
 
1.6%
13 6435
 
1.6%
10 6435
 
1.6%
81 6435
 
1.6%
21 6435
 
1.6%
67 6435
 
1.6%
79 6435
 
1.6%
46 6431
 
1.6%
74 6430
 
1.6%
11 6426
 
1.6%
Other values (71) 348390
84.4%
ValueCountFrequency (%)
1 6386
1.5%
2 6049
1.5%
3 6410
1.6%
4 6435
1.6%
5 6241
1.5%
6 5986
1.5%
7 6255
1.5%
8 6310
1.5%
9 6332
1.5%
10 6435
1.6%
ValueCountFrequency (%)
99 862
 
0.2%
98 5836
1.4%
97 6278
1.5%
96 4854
1.2%
95 4483
1.1%
94 5631
1.4%
93 5880
1.4%
92 3973
1.0%
91 6200
1.5%
90 5432
1.3%

weekly_sales
Real number (ℝ)

Distinct350619
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13852.381
Minimum-4988.94
Maximum84112.78
Zeros73
Zeros (%)< 0.1%
Negative1285
Negative (%)0.3%
Memory size3.1 MiB
2025-03-10T22:10:07.708685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4988.94
5-th percentile57.6705
Q11997.7525
median7295.49
Q318978.175
95-th percentile52321.189
Maximum84112.78
Range89101.72
Interquartile range (IQR)16980.423

Descriptive statistics

Standard deviation16884.597
Coefficient of variation (CV)1.218895
Kurtosis2.9635412
Mean13852.381
Median Absolute Deviation (MAD)6441.185
Skewness1.7980414
Sum5.7171823 × 109
Variance2.8508963 × 108
MonotonicityNot monotonic
2025-03-10T22:10:07.819972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 353
 
0.1%
5 289
 
0.1%
20 232
 
0.1%
15 215
 
0.1%
12 175
 
< 0.1%
1 169
 
< 0.1%
10.47 167
 
< 0.1%
11.97 154
 
< 0.1%
2 148
 
< 0.1%
7 146
 
< 0.1%
Other values (350609) 410674
99.5%
ValueCountFrequency (%)
-4988.94 1
 
< 0.1%
-3924 1
 
< 0.1%
-1750 1
 
< 0.1%
-1699 1
 
< 0.1%
-1321.48 1
 
< 0.1%
-1098 3
< 0.1%
-1008.96 1
 
< 0.1%
-898 1
 
< 0.1%
-863 1
 
< 0.1%
-798 4
< 0.1%
ValueCountFrequency (%)
84112.78 1
< 0.1%
84110.58 1
< 0.1%
84110.3 1
< 0.1%
84106.09 1
< 0.1%
84102.25 1
< 0.1%
84099.9 1
< 0.1%
84097.87 1
< 0.1%
84097.64 1
< 0.1%
84092.89 1
< 0.1%
84090.01 1
< 0.1%

holiday
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0
383804 
1
 
28918

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters412722
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

Length

2025-03-10T22:10:07.915178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T22:10:07.994228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 383804
93.0%
1 28918
 
7.0%

type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0
208029 
1
162264 
2
42429 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters412722
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

Length

2025-03-10T22:10:08.105657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T22:10:08.185197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 412722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 208029
50.4%
1 162264
39.3%
2 42429
 
10.3%

size
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135728.18
Minimum34875
Maximum219622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-03-10T22:10:08.296492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum34875
5-th percentile39690
Q193638
median128107
Q3202505
95-th percentile206302
Maximum219622
Range184747
Interquartile range (IQR)108867

Descriptive statistics

Standard deviation60954.044
Coefficient of variation (CV)0.44908907
Kurtosis-1.2164887
Mean135728.18
Median Absolute Deviation (MAD)70910
Skewness-0.30318316
Sum5.6018008 × 1010
Variance3.7153955 × 109
MonotonicityNot monotonic
2025-03-10T22:10:08.423865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
39690 20728
 
5.0%
39910 20583
 
5.0%
203819 19741
 
4.8%
158114 10213
 
2.5%
203007 9973
 
2.4%
207499 9967
 
2.4%
114533 9959
 
2.4%
202505 9902
 
2.4%
123737 9895
 
2.4%
155078 9886
 
2.4%
Other values (30) 281875
68.3%
ValueCountFrequency (%)
34875 8997
2.2%
37392 8904
2.2%
39690 20728
5.0%
39910 20583
5.0%
41062 6671
 
1.6%
42988 7156
 
1.7%
57197 9435
2.3%
70713 9758
2.4%
93188 9812
2.4%
93638 9450
2.3%
ValueCountFrequency (%)
219622 9813
2.4%
207499 9967
2.4%
206302 9873
2.4%
205863 9569
2.3%
204184 9710
2.4%
203819 19741
4.8%
203750 9704
2.4%
203742 9393
2.3%
203007 9973
2.4%
202505 9902
2.4%

Interactions

2025-03-10T22:10:01.616370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.047157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.795027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.410888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.123871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.709354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.470147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.236485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.104856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.942607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.730317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.348805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.981817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.754354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.158376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.906256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.537268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.236831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.836638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.596231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.394375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.225106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.095995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.854073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.460590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.109306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.886022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.285888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.032282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.664265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.347799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.964217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.707800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.527224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.342344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.207210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.968323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.602853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.237368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.045333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.418523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.158929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.791843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.492266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.091261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.861542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.695938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.519903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.350104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.111215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.730523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.374074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.172001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.523966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.270513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.935396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.601177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.218399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.980626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.837817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.642640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.461316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.233852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.856918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.490845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.299410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.650594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.406124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.062860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.727990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.331376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.103427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.964650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.816299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.614135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.348117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.983072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.618026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.426364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:41.920660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.524149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.201769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.855088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.599954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.237373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.091668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:53.958813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.741449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.486259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.109104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.751346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.574313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.047782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.666701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.333003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:46.981858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.735613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.357825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.283234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.139173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:55.857296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.601804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.235401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:00.871329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.696632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.177636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.795103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.459361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.107416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.852851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.484424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.408259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.258270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.126476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.728225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.367493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.013748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.823741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.286974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:43.904111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.592895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.229219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:48.979270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.635171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.537216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.381141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.242365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.855002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.489640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.125022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:02.997891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.414758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.044036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.730424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.344966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.104063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.793723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.665177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.542053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.365071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:57.981648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.600935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.251649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:03.266984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.525895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.164024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.855284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.471670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.217266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:50.951235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.835274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.668911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.476306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.093711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.728404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.378883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:03.395209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:42.666602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:44.284329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:45.982763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:47.582853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:49.343533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:51.089046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:52.962110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:54.801549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:56.603044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:58.224578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:09:59.855152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T22:10:01.489710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-10T22:10:08.502814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cpideptfuel_priceholidaymarkdown1markdown2markdown3markdown4markdown5sizestoretemperaturetypeunemploymentweekly_sales
cpi1.000-0.013-0.0420.0120.1850.1500.1680.1760.195-0.000-0.2360.1730.185-0.388-0.022
dept-0.0131.0000.0030.0000.0030.0020.0030.0010.003-0.0060.0170.0010.0820.008-0.049
fuel_price-0.0420.0031.0000.1370.4690.2960.4030.4280.4440.0040.0740.1270.089-0.0580.003
holiday0.0120.0000.1371.0000.0390.2200.2810.0720.0330.0000.0000.1870.0000.0350.009
markdown10.1850.0030.4690.0391.0000.7960.9030.9530.9680.075-0.034-0.0190.093-0.2270.026
markdown20.1500.0020.2960.2200.7961.0000.7250.7890.7870.102-0.056-0.1290.045-0.1830.032
markdown30.1680.0030.4030.2810.9030.7251.0000.8690.9050.086-0.029-0.0730.037-0.2060.034
markdown40.1760.0010.4280.0720.9530.7890.8691.0000.9240.137-0.095-0.0340.053-0.2190.053
markdown50.1950.0030.4440.0330.9680.7870.9050.9241.0000.082-0.021-0.0280.057-0.2410.026
size-0.000-0.0060.0040.0000.0750.1020.0860.1370.0821.000-0.157-0.0430.851-0.0640.275
store-0.2360.0170.0740.000-0.034-0.056-0.029-0.095-0.021-0.1571.000-0.0560.5400.296-0.094
temperature0.1730.0010.1270.187-0.019-0.129-0.073-0.034-0.028-0.043-0.0561.0000.1240.030-0.018
type0.1850.0820.0890.0000.0930.0450.0370.0530.0570.8510.5400.1241.0000.1790.156
unemployment-0.3880.008-0.0580.035-0.227-0.183-0.206-0.219-0.241-0.0640.2960.0300.1791.000-0.014
weekly_sales-0.022-0.0490.0030.0090.0260.0320.0340.0530.0260.275-0.094-0.0180.156-0.0141.000

Missing values

2025-03-10T22:10:03.535595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-10T22:10:03.964087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

storedatetemperaturefuel_pricemarkdown1markdown2markdown3markdown4markdown5cpiunemploymentdeptweekly_salesholidaytypesize
012010-02-0542.312.5720.00.00.00.00.0211.0963588.106124924.5000151315
1352010-02-0527.192.7840.00.00.00.00.0135.3524619.262314612.1901103681
2352010-02-0527.192.7840.00.00.00.00.0135.3524619.262426323.1501103681
3352010-02-0527.192.7840.00.00.00.00.0135.3524619.262536414.6301103681
4352010-02-0527.192.7840.00.00.00.00.0135.3524619.262611437.8101103681
5352010-02-0527.192.7840.00.00.00.00.0135.3524619.262723416.2401103681
6352010-02-0527.192.7840.00.00.00.00.0135.3524619.262827545.3801103681
7352010-02-0527.192.7840.00.00.00.00.0135.3524619.262912454.6101103681
8352010-02-0527.192.7840.00.00.00.00.0135.3524619.2621015052.4601103681
9352010-02-0527.192.7840.00.00.00.00.0135.3524619.262257523.1501103681
storedatetemperaturefuel_pricemarkdown1markdown2markdown3markdown4markdown5cpiunemploymentdeptweekly_salesholidaytypesize
412712132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621412820.2800219622
412713132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621427707.6800219622
412714132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.6214412780.0200219622
412715132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.6214638219.8900219622
412716132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621481241.0000219622
412717132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621497770.7100219622
412718132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621501486.0000219622
412719132012-10-2646.973.75510192.49364.57150.01714.155563.92131.1930975.621524738.9300219622
412720412012-10-2641.803.6864864.30101.34250.647.241524.43199.2195326.195432699.7800196321
412721452012-10-2658.853.8824018.9158.08100.0211.94858.33192.3088998.667981076.8001118221